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A n Evaluat ion of Intelligent Systems For Fault DiagnosisJamesA. Momoh Dijan Sobajic JamesDolceHoward University Palo Alto,California Cleveland,OhioWashington, D.C. 20059Department of Electrical Engineering Electric Power Research Institute NASA-LeRC

A bssract: This paper discusses the foundation ofIntelligent Systems sufficient for studying spacestation and/or utility power systems, where faultdetection are of paramount importance.

The functionality of these IntelligentSystems or toois, such as expert systems,artificial neural networks and fuzzy logic, foraiding in design, control and analysis of powersystems diagnosis is emphasized. The argum entfor selecting them in terms of their abilities andplatforms for using them ar e discussed. Finallyresearch activities using these tools for bothspace power systems (Space Station Freedom) andterrestrial distribution systems in a major utilityare presented.The evaluation has shown that theIntelligen t Systems hold significan t promise foron line fault analysis or diagnosis. Crite ria forusing these tools as embedded took in classicalmethods of faul t study ar e discussed. Intelligen tsystems are useful for managing input data andinterpreting faulty results.

1. INTRODUCTIONThecomputation requirements and different levelsofuncertainties by conventioral (classical) methods of faultanalysis are approximately several magnitudes higher thanthose of single heuristic based techniques reported. These

high requirement and typically off-line approaches make itinfeasible to implement a real time fault analysiddiagnoskscheme. Novel methods that stand alone exist with thecapability to clearly define the location of faults, classifyfaults types, and recommend corrective actionsU, remove thecause of faults. These novel methods are embedded inclassical diagnosis techniques to minimize the computat idburden.The complexity in detecting fauIts, especially faultson the electric power subsystem(EPS)of the Space StatioriFreedom, and the presenceof arcing and non-arcing faults inutility distribution systems is of prime concern for thesurvival and security of the respective systems. Recognizingthese complexities, NASA and EPRI. and utility companies

including LADWP, Puget Sound Electric Company andseveral otherssponsored several research projects to definetheresearch needs, scope and demonstration projects.Subsequently, several research projects have been initiatedover the years by vendors, universities and governmentagencies including NASA and EPRI, that recognize the

potential of intelligent tooh fc-h power systen; andjsis ,planning and operation. This paper evaluates the ability ofintelligent systems(IS) for fault studies.

To address these problems, a number of papers andtext exist covering the foundation of the various methods, theconcepts behind them and their suitability for fault studies.Examples of such works reviewed can 'be found in[2,1%,17,18,30,31]. The organization of this paper is asfollows:(a)(b)(c)

Classical methods of fault anaiysis.Intelligentmethods for fault analysis.Functional and basic concepts of intelligenttook that are considered effectivefor faultdiagnOSiS.

The evaluation scheme for adopting these tools forfault study is described. A step by step procedure is clearlyshown. Finally resu!ts of an going fault diagnosis workusing these tools isdiscussed.The examples illustrated includes faults in utilitydishbution systems and onboard ihe space station. Thespace station feeder typically consistsof sources DDCU @c-to-DC converter), lines between power sources andexperimental loads which can number in the hundreds for theSpace Station Freedom.The breakdown of the different classes of faults in aspace station or a utility distribution system, systems couldoperate in AC or DC modes. Therefore, a fault description

tree for a typical power system is shown in Figure 1.It consists mainly of fault types in AC and DC for eachsystem.

Figure 1. Fault DescriptionTree DiagramEvaluation of fault analysis techniques will bedivided into two classes. namely, classical approaches and IS

approach-

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11.1 hThe classical algorithmic approaches uses thevoltage, current relations, and energy contents of the signalduring and after the fault, to identify fault types and in somecases to detect arcing and non-arcing conditions (utilitydistribution systems), whereas in the space station powersystem, the standard V- I relationship characterizing thedevices is used to isolate the faulty feeder.The techniques currently considered to be classicalare:

( 1) These techniques compute magnitude and phaseangle (built by PTI, Hughes Aircraft, Norden Research tDevelopment), sponsoredby EPRI and recent ones have beenexpanded to handle arcing using the 3rd HarmonicApproach. 1221(2) Built by Texas AIM under an EPRI Project, t uses aprocessor based energy algorithm for detection of suddenincrease in the level for a given frequency or band offrequency (surplus). 7I is method cannot detect all arcingfaults. Another version of the energy technique is called theRandomness Algorithm, which has the potential to detectintermittent arcing situations or low and high levels ofenergy with respect to frequency. [lo](3) The ratio of neutral current to thepositive sequencecurrent is detected. This signal is proportional to the rootmean square value of the ground current. Developed by Carr,this technique detects the downed conductor conditions offaults.[23] This method c8n only detect 8045% of faultyfeeders.(4) Uses the load analysis algorithmto monitor patkmsin load current which gives a good indicatorof a trippedfeeder[U]. Other methods using hypothesis testing, symmetricalcomponents of lst, 3rd & 5th harmonics, give indications oflow current arcing faults and can be computationallyintensive. Ground faults / open faults using proportionalrelaying also exist.11.2. Jntelv-

The intelligent support scheme using artificialintelligence (AI)approaches or techniques for fault analysishave a number of characteristics in common. They are aimedat verifying concepts or improving decision maling. Theyconsider small system models for prototyping. They areeacily meant to be embedded in existing classical tools forfault analysis. Recent activities using non-algorithmapproaches for fault study include, expert systems (ES),artificial neural networks (ANN),and fuzzy logic (FL).

model based on simulation of fault types.[lO] It is mainlybased in information from operation of protection relays,tripped circuit breakers which are readily available and usableby experiencedoperators. [121.Each of these methods uses ES framework.Incapability to detect arcing and location of Eaults, which useto pose Ufxulty,are overcome by the ES techniques. TheES echniquesare n general useful for fault estimation.The ANN based approaches have proven to beattractive in reportedwork. They have been used to classifyfault types and are able to detect arcing snd non-arcingcombinations. The ANN approaches are far superior toexisting classical techniques.The work sited by [171uses a four layer perceptronnetwork, and the back propagation learning algorithm.Recent work by Momoh and Butler uses the clusteringbasedapproach for arcing fault detection.[21] The work byFernando andWatson combines AN N and FL to improve anfault detection inan arcing environment[25]The FL based approach for fault detection aims at

improving on decision making by eliminating ambiguity intennsof severity and location of fault. Work reported in [151isa good representation of thismethod.

The following system requirements are relevant fordeveloping or assessing each of these IS. They are brieflymentioned h a :(a) I&I&ifblimofstates.(b) selectivity of controls.(c)(a) coordinationoftasks.(e) Flexibility.(f ) Ability to handle uncertainty.

Leammg ability to update knowledge.

Each of the intelligent systems are described basedon their functionality. Figures 2.3 and 4 show a functionaldescription of relatedcomponentsused to build the IS.

Figure 2. Types of ANN FunctionalityThe reported work using ES computes multipleenergy parameters, generates a rule based scheme from a

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Lisp. C, C++, ortran77 SmallTalk. KDL, VL, t cetera.Table 1 encapsulates the implementations of some of thesetools.

DeveloperNware

ButlerA L W m(Sobajic/Pao)ware

toextemalappCcatronusB( suppott graphics

Types of ES Functionalityigure 3.

PlarfomPCMacSunVax/PCsun/vaxPCP c m cSun

Figure 4. Types of FL FunctionalityIII. OVERVIEW OF INTELLIGENT SYSTEMS FORFAULT DIAGNOSISIn.1

The function of the ES consists of its ability tocollect and store in a computer an experts ability to solve aproblem so that a nonexpert can use it. The functionalcomponents of an ES and he scheme for building a particularone for fault diagnosisatediscussed next.(1) Knowledge Base: Contains a l l the pattems of the

fault model describing devices /feeders orbus faults,transformer faults and other electrical networkcomponents.(2) User Interface: Inputloutput or so called man-machine interface gives the necessary informationabout some relays, circuit breakers and systemstopology and decision rules to the operators orInference Engine: The analysis of fault based on theknowledge of states of this system (model ofdifferent faults data base) the sequence of possiblefaults using if then roles based on gWdata aientedstrategy called forwardbackward chainingrespectively is used

dispatchers.(3)

Other modules such as control mechanism and modificationloops are usually included o achieve a robust expert system.Languages and tools used for E 3 developed in faultstudy includes the following: KEE, Nexpert, OPS5.0PS83,ART, KES, Goldworks, SNAP,ADA, PROLOG, CORE,

ES Tools for Fault Analysisable1.IES I N d I P l a t f m I DeveloperApplicationAC faults

DC faultsControls

LanFG*OF55=OB83G 2"expert*ADA*ADAG2ONexpext-0PS5*C++*MathlabGCLisp

van, Pcvax, PCvax,Pcall platformsall platformsall platformsvax. PC

all platformsPC,Mac

GenSymGenSymN e d a t aVariesVaries

GenSymVariesMathworksGolden Hill. .III2

ANNs canbe defined by three elements; namely theneurons, the specific topology of weighted interconnectionsbetween the neurons,and a learningalgorithm which providesfor thevalidity of the intexwnnection weights. They are basedon a parallel distributed architecture. The steps for usingANN are as follows:a.b. NormalizedataC. Select ANNarchitecture.d Train ANN.e. Test ANN.

Collect representative data for trainingpurpose.

In addition, a proper selection of a network architecture andperformance analysisof selected training sets is done.A survey of the several types of ANNS that can beused for fault analysis including back propagation, clustering

are dentified in Table 2.Table 2.ANNApplicationAC faults

DC faultsControls

ANN TocName-Backpropagaton=clustering*NewsightPowetCombustion*Badrpropagaton*Clustering*processopumization

-2k2k3kN/A30k10k2k3k-/A5klk---

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1 '.m.3Fuzzy set theory offers new methods for modelingthe inexactness and uncertainty concerning decision making.TheFL approach improves the potential for modeling humanreasoning and for presenting and utilizing linguisticdescriptions in a computerized inference. Two methods of

developing fuzzy modelsarebased on:1.2.

Laws of causeand effect which use rulesof relationdescribed by reasoning in variable sets theory.Laws of transition which use ordinary algebraicequations o express cause and effect relationship(fuzzy sets are used for variables).Clearly, fuzzy set theory uses the concept ofpossibility defined as a number between one (completelypossible) and zero (totally impossible). This is in contrast toprobability which appropriately measures the uncertainty ofstatistical information available. While probability failswithout statisticaldata (for example the failure rateof feedefi)in fault diagnosis, fuzzy set theory does a better job thanother IS.A mathematical formulation for fuzzy set theory isdefinedas follows:If x is a collection of objects, such as faulty

elements denoted generically by x, then a fuzzy set A in x sof ordered pairs A = {x,p,,(x$ E X) where p is calledthe membership function,or grade of membershipof x in A.

In a fault situation, the basic crisp set on theuniverse of X consistsofall components of the faulted feeder.The two crisp sets are given: sfd and Sdd, theintersection of which is empty set:

x =SfdUSd&f& G ,SmUmIISfdnS+&=O

Use of optimization with fuzzy sets via theirmembershipfunctionsare well defined. Far example:1.

For the operator in the control center who does notknow the exact location of a fault, a fuzzy set model istypically formed. The concepts of fuzzy set theory are in[263. A scheme for developing a fuzzy based fault &agnosistool in space or temuial power systems is the subject ofongoing research. Basically, it consists of:

1. Heuristic knowledge and information concerningfault situations modeled as membership functionsof fuzzy sets.Combine fuzzy set alternatives.Location of thefaults canbe obtained and arrangedaccordingto theirpossibility based on availabie information on thefault situation.Select locations of faults and donecessaryswitching.

2.

3.Table 3lists some of the fuzzy logic tools that canbe used fa r fault analysis.

Table 3. FL Tools Suitable for Fault AnalysisTool IDeveloper IPlatform IsCubicalc 1Hc ( M S I 495- - Iwindows 1Tilshell I Togai I PC,MAC, I8,900

IV. SOME LLUSTRATIVEEXAMPLES OFINTELLIGENTSYSTEMS FORFAULTANALYSISIV.l --

he Autonomous Power Expert (APEX) ComputerProgram is a prototype expert system program developed byNASA Lewis Research Center. It detects faults in anelectrical power distribution system. It is written in LISPand executes on a computer workstation capable of receivingdata from sensors within a 2OkHz power distribution testbed-[lI

APEX includes a knowledge base that contains factsand rules acquired from a human expert. APEX data basecontains sensory data and esults of calculationsbasedon dataItcon" n inferenceengine basedon reasoningmechanismwhich draws conclusion from information stored in theknowledgebase and also provides the reasoning capability tochoose appaopiate ecovery capability. APEX isalso capableof simulating data to enable testing when the test bed isunavailable. An external scheduling computer generatessource profiles that represent power sources that will beavailable during a specified time and load profile to indicateRow much power the loads consumes during the same time.Another working example using Intelligent Systemsis the model based diagnostics for the Space Station Freedom.The roo1 called MARPLE was used lo develop and run thefault management system.[27] MARlPLE's capabilities are

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similar to those of other expert system shells, except that itis model based rather than rule based. It was developed byTRW; and tested at NASA Lewis Research Center. Thisapproach is one of several techniques being tested for faultmanagement. The version of MARPLE used for the SpaceStation project is written in LISP and runs on a TexasInstrument0 icroExplorer. (a Macintosh II rnachine witha TI Lap board). MARPLE contains all functions needed torun a model based system.

The model-based expert system was tested using aVAX based simulation program. This program computes theequivalent system state via load flow and state estimation tocompare against fault quantities detected by the expeat system.The success of th is prototype demonstrates the feasibility forapplying model basedThis model based expert system can easily be usedfor diagnosis of terrestrial systems.

to power system diagnosis.

IV.2 -eSaSab 'on Freedomeural

A clustering based artificial neural networkalgorithm has been developed fox fault analysis. Theprototype result reported in a previous NASA contract byDolce, Sobajic and Pa0 is capable of analyzing systemsecurity during a given contingency.[28] Extensions weremade to the clustering based ANN algorithm, developed byButler and Momoh, by modification of the stabilization phaseof the clustering algorithm and combination of the ANN witha statistical feature extraction module to detect arcing faults indelta-delta distribution systems.The scheme is currently being adapted to SpaceStation Freedom fault detection and classificationtypes. TheANN scheme for non-terrestrial power system fault analysisis at the developmental stage at Howard University under agrant h m NASA Lewis Research Center. It classifies fault

on a simple 5 bus system represented by a secondary powerdistribution unit and tertiary power distribution units(TPDU). The detectionof fault type and location is done byusing the SPICE circuit emulation program to simulate thesystem topology, oc test bed,and to generate the fault types..The representative voltageand c m t rojectionsareextendedand trainedvia theANN clusteringbased technique.The location and fault type of an unknowncontingency are determined via an ANN consultationphase.[29] The ANN program which executes on a VAXplatform was developed in the FORTRAN77 and Cprogramminglanguages.. .IV.3 A FL based example for fault location on a

distribution system has been built by Jarventauta, et al.[15]It deals with uncertainty involved in the process of locatingfaults in a distributionnetwork. The prototype scheme servesand support tools for real time fault diagnosis. It employsthe heuristic knowledgeof theexpert control center operators.

The current version fo r real time approach is basedon a C++ program executing on a 386 PC machine. Theprototype is co ~ ec tedo the SCADA system. The schemeobtains network data of the faulted feeder r e d from thenetwork data base system using the real time switching statusand borrowing information from SCADA system. By usingb&uristic knowledge and infomation concerning the fault,systems are modeled as various inembership functions offuzzy sets. By combining these fuzzy sets, altemative placesof the fault can be obtained and arranged according to theirpossibility based on the available information on the faultsituation.

Various methods can be used to select the mostlikely fault location. In our on going work at HowardUniversity, an expert system based scheme is used fo rlocation of faults in distribution systems.[30] The exampiesdiscussedareeasily adaptableto faulty situations in terrestrialor spaceelectrical power subsystems.V. CONCLUSIONS AND RECOMMENDATIONS

Considering the main finding of our evaluation, wecan concl& that most of the existing IS for fault detectionare based on the first generation of intelligent systems. Theapplication tools are mostly in favor of Es. The consequenceof this a p p r c s h is that they are rather system specific and arem-

A few examples hased on models are favored forfuture expert system ksed schemes, The combination ofexpert system (model) basedschemes and state estimation isconsidered to be an area of future research. The ANN basedtypes have gamed recent interest Several prototype examplesare promising in their ability to classify and estimate faults inboth average and non averaging environments. Estimates oferroneous data or uncertainty using neural networks are areasof future work.Fuzzy set theory is relatively new as a fault detectiontool. Further validation of membership functions and use ofnetwork operation support scheme using real fault situationsisbeing examined by several researchers.A standardized method for building intelligentsystems for fault study/development is essential in thisreport. The methodologies discussed in the paper, while notexhaustive is a major source of inspiration for futuredevelopers of IS for fault diagnosis. The aim is that it willacceaerate the introduction of IS to the modeling culture ofdistribution fault diagnosis.Some practical actions could be considered withoutpretendingto be exhaustive, we suggest

1. Organize session at IEEE sponsored tutorials onintelligent systems.2. Introduce IS courses to the curriculum ofuniversities.41 7

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3 . Deploy prototype working IS support scheme foroperators at substations and control centers forevaluation,ACKNOWLEDGEMENTS

Th e authors wish to acknowledge the support ofNASA (NAGS-1426) and NSF/RIMI (HRD-9253055) nthis endeavor.

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